The weather has a profound effect on the personal and economic lives of people. Its pervasiveness affects various decisions made by individuals on how to order their affairs, optimize economic activity, and plan for future events. Different weather events have different significance, depending upon the activity involved. For example, lightning is a weather event that is known to be responsible for deaths and injuries, forest fires, personal and business property losses, and airline delays. The ability to predict the probability of lightning strikes is of great importance since it allows informed decisions to be made and proactive measures to be taken, in order to mitigate the negative economic and human impact resulting from these events. For example, forest fire fighters might be dispatched to areas with high probability of lightning strikes so that they could rapidly handle any fire that might be caused by the lightning strikes. The same scenario would hold true for the prepositioning of repair crews and equipment for electrical power outages caused by lightning strikes. Airlines would be able to better schedule and reroute flights to accommodate adverse circumstances caused by lightning strikes.
Similarly, all other weather-related events, such as snowfall, heavy rain and flooding, large hail, damaging wind, and tornadoes, may have different but equally significant affects on various activities, human and economic. Many resources have been devoted to improving the ways in which the probability of occurrence of these weather-related events can be forecast. In order to forecast such weather-related events, data must be collected and analyzed on a real-time basis. The data collection may produce an enormous database that may exhibit little uniformity as to physical spacing, accuracy, and time sequencing of the measurements. The accuracy and extent of these data may vary from region to region depending upon the facilities available and the importance of having an accurate forecast to those personnel in the region.
Data must be obtained from which to forecast the probability of weather events for a given region. Currently, the Rapid Update Cycle 2 (RUC2) model is one method used for measuring the atmosphere across the United States and providing a useful data set for prediction of weather events. Becoming operational in 1998, the RUC2 model represented the first time that an hourly, 3-dimensional data set became available. It contains the parameters necessary for evaluating the potential for convection as well as heavy snow, rain, severe weather or any other selected weather event, i.e. pressure, temperature, moisture, and wind parameters. Another such model is the ETA Model, one of the operational numerical forecast models run at the National Centers for Environmental Prediction (NCEP). The ETA model is run four times daily, with forecast output out to 84 hours. Yet another model under development is the Weather Research and Forecasting (WRF) model.
There have been numerous methods developed for predicting weather-related events. One recent effort at lightning prediction for the zero to three-hour period is the System for Convection Analysis and Nowcasting (SCAN; Smith et al. 1998). This technique is an extrapolative-statistical method based on three years of lightning, radar, and satellite developmental data. The algorithms use extrapolated forecasts of radar reflectivity, satellite-estimated cloud-top temperatures, and current lightning strike rates. However, these algorithms do not extend very far into the future because they rely so heavily on the extrapolation of current conditions. They also are unable to predict when and where new storms will develop. With the exception of these extrapolative forecasts, no short-term (0 to 6 hour) lightning forecasts are in operational use. In addition, previous attempts, using Model Output Statistics or MOS, have tied predictive equations to specific models, which cannot change as the models themselves change and improve. Other similar methods are given according to U.S. Pat. No. 6,405,134, to Smith, entitled “Method and Apparatus for Predicting Lightning Threats Based on Radar and Temperature Data”, that provides forecasts of one hour or less.
While sophisticated computer models predict many surface parameters, such as surface wind (10 meter), surface temperature, and dew point (2 meter), other important quantities are not forecast directly. Fields such as amount of cloud cover; ceiling; visibility; probability and type of precipitation; thunderstorm probability; and severe thunderstorm probability are not explicitly predicted by most models. Two basic methodologies have been used to compute fields not specifically forecast by a model. These are known in the art as the Model Output Statistics (MOS) and the Perfect Prog (prognosis) methodologies.
According to the Perfect Prog methodology, a statistical relationship is developed between the variable to be estimated and selected variables which can be forecast by a model. The predictors and predictand are observed quantities in the developmental sample. This relationship is applied using the output fields from the forecast models as predictors to estimate the predictand, treating the model forecast as a perfect prognosis. As improvements to the model are made, forecasts of the predictors should improve with corresponding improvement of the Perfect Prog forecasts.
According to the MOS methodology, a statistical relationship is determined between the predictand and the predictors from a numerical model at a predetermined projection time. MOS equations in the past have been able to out-perform a Perfect Prog approach because they take into account model biases and the decline in the model accuracy with increasing forecast projection. The most serious drawback to the MOS methodology, however, is that it requires a stable numerical model. Given the rate with which changes and improvements are made to today's models, it is doubtful that any model will remain static long enough to develop new MOS equations. Alternatively, the use of the MOS methodology requires the newer version of any model to be “rerun” on a minimum of one year (and usually two to three years) of model output data, which may require expenditure of extensive data collection and computational resources.
Because today's complex computer forecast models change more frequently than those of the past, recent research in forecasting weather-related events has not been transferred to the MOS statistical forecast methodology. Other MOS-based statistical forecasts have ended when the particular model they were based upon was discontinued (e.g., the Limited area Fine Mesh (LFM) model). MOS equations derived from the older models such as the Nested Grid Model (NGM) have not been updated since the NGM code was “frozen” in 1991.
The basic problem is one of data compression. In other words, vast amounts of historical and real time data are now available through various models. So much data is available at different levels and times that it is often difficult to reduce and/or compress the data in a way that is meaningful for the particular weather-related event of interest. What is needed is an improved statistical prediction system for more accurately predicting the occurrence of selected weather events; for reducing or compressing the data by identifying meaningful parameters that are pertinent to the weather-related event being examined; for producing forecasts that bridge the existing gap between extrapolative systems and model-based systems by using both analyses and model forecasts; and for improving the general understanding of environmental characteristics which support the significant weather events